Hadoop学习---Eclipse中hadoop环境的搭建-CFANZ编程社区

Eclipse中hadoop环境的建立

在eclipse中建立hadoop环境的支持

1.需要下载安装eclipse

2.需要hadoop-eclipse-plugin-2.6.0.jar插件，插件的终极解决方案是https://github.com/winghc/hadoop2x-eclipse-plugin下载并编译。也是可用提供好的插件。

3.复制编译好的hadoop-eclipse-plugin-2.6.0.jar复制到eclipse插件目录（plugins目录）下，如图所示

Hadoop学习---Eclipse中hadoop环境的搭建_Hadoop

重启eclipse

4.在eclipse中配置hadoop安装目录

windows ->preference -> hadoop Map/Reduce -> Hadoop installation directory在此处指定hadoop的安装目录

Hadoop学习---Eclipse中hadoop环境的搭建_apache_02

点击Apply，点击OK确定

5.配置Map Reduce视图

window -> Open Perspective ->other-> Map/Reduce -> 点击“OK”

window -> show view -> other -> Map/Reduce Locations -> 点击“OK”

Hadoop学习---Eclipse中hadoop环境的搭建_hadoop_03

6.在“Map/Reduce Location”Tab页点击图标<大象+>或者在空白的地方右键，选择“New Hadoop location...”，弹出对话框“New hadoop location...”，进行相应的配置

Hadoop学习---Eclipse中hadoop环境的搭建_apache_04

设置Location name为任意都可以，Host为hadoop集群中主节点所在主机的ip地址或主机名，这里MR Master的Port需mapred-site.xml配置文件一致为10020，DFS Master的Port需和core-site.xml配置文件的一致为9000，User name为root（安装hadoop集群的用户名）。之后点击finish。在eclipse的DFS Location目录下出现刚刚创建的Location name（这里为hadoop），eclipse就与hadoop集群连接成功，如图所示。

Hadoop学习---Eclipse中hadoop环境的搭建_Hadoop_05

7.打开Project Explorers查看HDFS文件系统，如图所示

Hadoop学习---Eclipse中hadoop环境的搭建_hadoop_06

8.新建Map/Reduce任务

需要先启动Hadoop服务

File -> New -> project -> Map Reduce Project ->Next

Hadoop学习---Eclipse中hadoop环境的搭建_Hadoop_07

填写项目名称

Hadoop学习---Eclipse中hadoop环境的搭建_Hadoop_08

编写WordCount类：

package test;

import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
public class WordCount {
  public static class MyMap extends Mapper<Object, Text, Text, IntWritable> {
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
    @Override
    public void map(Object key, Text value, Context context)
        throws IOException, InterruptedException {
      StringTokenizer itr = new StringTokenizer(value.toString());
      while (itr.hasMoreTokens()) {
        word.set(itr.nextToken());
        context.write(word, one);
      }
    }
  }

  public static class MyReduce extends
      Reducer<Text, IntWritable, Text, IntWritable> {
    private IntWritable result = new IntWritable();
    @Override
    public void reduce(Text key, Iterable<IntWritable> values,
        Context context)
    throws IOException, InterruptedException {
      int sum = 0;
      for (IntWritable val : values) {
        sum += val.get();
      }
      result.set(sum);
      context.write(key, result);
    }
  }

  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    String[] otherArgs = new GenericOptionsParser(conf, args)
    if (otherArgs.length != 2) {
      System.err.println("Usage: wordcount <in> <out>");
      System.exit(2);
    }
    Job job = new Job(conf, "word count");
    job.setJarByClass(WordCount.class);
    job.setMapperClass(MyMap.class);
    job.setReducerClass(MyReduce.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
    FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}